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Record W1881382755 · doi:10.1002/hed.23490

Evaluation of shoulder disability questionnaires used for the assessment of shoulder disability after neck dissection for head and neck cancer

2013· article· en· W1881382755 on OpenAlexafffund
David P. Goldstein, Jolie Ringash, Éric Bissada, Yves Jaquet, Jonathan C. Irish, Douglas B. Chepeha, Aileen M. Davis

Bibliographic record

VenueHead & Neck · 2013
Typearticle
Languageen
FieldMedicine
TopicNerve Injury and Rehabilitation
Canadian institutionsUniversity Health NetworkPrincess Margaret Cancer CentreToronto Rehabilitation InstituteUniversity of Toronto
FundersCanadian Institutes of Health ResearchCancer Care Ontario
KeywordsHead and neck cancerMedicineNeck dissectionPhysical therapyPhysical medicine and rehabilitationCancerInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Several questionnaires have been used to evaluate shoulder disability after neck dissection. The purpose of this study was to review these measures and highlight their strengths and weaknesses. METHODS: A literature review was performed to identify measures of shoulder disability after head and neck cancer surgery. These measures were evaluated in terms of their methods of development and assessment of their psychometric properties. RESULTS: Seven questionnaires were identified. Several of the other questionnaires have been well developed but have not had their psychometric properties assessed in the head and neck cancer population. Each questionnaire has its strengths and weaknesses. CONCLUSION: The strengths and weaknesses of the shoulder disability questionnaires should be considered when deciding which questionnaire to use. Efforts should be focused on using well-designed questionnaires that have been assessed in this patient population rather than developing or using other questionnaires.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.553

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.056
GPT teacher head0.425
Teacher spread0.368 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations21
Published2013
Admission routes2
Has abstractyes

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